| 丁明亮,宋娟,赵树飞,郁章伟.基于八激励模式数据融合的电阻抗成像优化算法[J].电子测量与仪器学报,2025,39(6):284-292 |
| 基于八激励模式数据融合的电阻抗成像优化算法 |
| Electrical impedance tomography imaging algorithm based oneight-modeexcitation mode data fusion |
| |
| DOI： |
| 中文关键词: 电阻抗层析成像 图像重建 八模式激励 电流激励 电压测量 数据融合 |
| 英文关键词:electrical impedance tomography image reconstruction eight-mode incentives current excitation-voltage measurement data fusion |
| 基金项目:山东省自然科学基金(ZR2021MF083）项目资助 |
|
|
| Author | Institution |
| Ding Mingliang | School of Engineering, Qufu Normal University, Rizhao 276826, China |
| Song Juan | School of Engineering, Qufu Normal University, Rizhao 276826, China |
| Zhao Shufei | School of Engineering, Qufu Normal University, Rizhao 276826, China |
| Yu Zhangwei | School of Engineering, Qufu Normal University, Rizhao 276826, China |
|
| 摘要点击次数: 745 |
| 全文下载次数: 683 |
| 中文摘要: |
| 电阻抗层析成像(EIT)是一种无损可视化检测技术,具有无辐射、实时、便携、成本低等优点,目前在工业检测和医学监护等方面应用较为广泛,但EIT技术也具有低分辨率等缺点,这也极大限制了EIT技术的快速发展。针对电阻抗成像过程中因“软场”效应及欠定性导致的重建图像内部目标数量不明确以及伪迹过大等问题,提出了一种八模式数据融合的电阻抗成像优化算法,根据8种激励模式各自成像的特点,借助重建图像和实际分布之间的相关系数对测量值进行权重训练,将权重矩阵同8种单一模式下得到的测量值矩阵进行融合,再通过Tikhonov正则化算法利用该矩阵进行成像。仿真结果表明,该算法能够有效地提高Tikhonov正则化算法重建图像分辨率,融合后的重建图像的相关系数平均提高了19.86%,相对误差平均降低了28.89%。由此表明,相比于传统的8种单一模式下的成像,该研究提出的算法在重建图像目标的数量、大小以及位置精确度等方面都得到提高,为EIT技术在医学和工业等领域的应用实践提供了新的理论依据和技术参考。 |
| 英文摘要: |
| Electrical impedance tomography (EIT) is a non-destructive visual detection technology, with no radiation, real-time, portable, low cost and other advantages, currently widely used in industrial testing and medical monitoring. But EIT technology also has low resolution and other shortcomings, which also greatly limits the rapid development of EIT technology. In this paper, aiming at the problems of unclear number of internal targets and excessive artifacts in the reconstructed image due to the “soft field” effect and under characterization in the process of electrical impedance imaging, this paper proposes an eight-modal data fusion electrical impedance imaging optimization algorithm, according to the characteristics of the eight excitation models of each imaging, with the help of the correlation coefficient between the reconstructed image and the actual distribution, the weight matrix is fused with the measurement value matrix obtained in eight single modes. The matrix was then used by the Tikhonov regularization (TR) algorithm for imaging. The simulation results show that the algorithm can effectively improve the resolution of the reconstructed image of the Tikhonov regularization algorithm, and the correlation coefficient of the reconstructed image after fusion is increased by 19.86% on average, and the relative error is reduced by 28.89% on average. This shows that compared with the traditional imaging under eight single models, the algorithm proposed in this paper has improved the number, size and position accuracy of reconstructed image targets, which provides a new theoretical basis and technical reference for EIT technology in the application practice of medical and industry and other fields. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
